التفاصيل البيبلوغرافية
| العنوان: |
Identifying Preventable Emergency Admissions in Hospitals Using Machine Learning. |
| المؤلفون: |
Alkhodair SA; IT Department, CCIS, King Saud University, Riyadh, Saudi Arabia., Altwaijri N; IT Department, CCIS, King Saud University, Riyadh, Saudi Arabia., Albarrak AI; Medical Informatics and E-Learning Unit, Medical Education Department, RCHIP, College of Medicine, King Saud University, Riyadh, Saudi Arabia. |
| المصدر: |
Studies in health technology and informatics [Stud Health Technol Inform] 2023 Oct 20; Vol. 309, pp. 95-96. |
| نوع المنشور: |
Journal Article |
| اللغة: |
English |
| بيانات الدورية: |
Publisher: IOS Press Country of Publication: Netherlands NLM ID: 9214582 Publication Model: Print Cited Medium: Internet ISSN: 1879-8365 (Electronic) Linking ISSN: 09269630 NLM ISO Abbreviation: Stud Health Technol Inform |
| أسماء مطبوعة: |
Original Publication: Amsterdam ; Washington, DC : IOS Press, 1991- |
| مواضيع طبية MeSH: |
Emergency Service, Hospital* , Hospitalization*, Humans ; Hospitals ; Machine Learning |
| مستخلص: |
Overcrowding in EDs has been viewed globally as a chronic health challenge. It is directly related to the increased use of EDs for non-urgent issues, leading to increased complications, long waiting times, a higher death rate, or delayed intervention of those more acutely ill. This study aims to develop Machine Learning models to differentiate immediate medical needs from unnecessary ED visits. A Decision Tree, Random Forest, AdaBoost, and XGBoost models were built and evaluated on real-life data. XGBoost achieved the best accuracy and F1-score. |
| فهرسة مساهمة: |
Keywords: Machine Learning; Overcrowding; Preventable Emergency Admissions |
| تواريخ الأحداث: |
Date Created: 20231023 Date Completed: 20231101 Latest Revision: 20231101 |
| رمز التحديث: |
20231215 |
| DOI: |
10.3233/SHTI230747 |
| PMID: |
37869814 |
| قاعدة البيانات: |
MEDLINE |
